{"id":"W4402703592","doi":"10.1101/2024.09.11.612340","title":"A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Discovery Centre","funders":"","keywords":"Virtual screening; Drug discovery; High-throughput screening; Robustness (evolution); Throughput; Deep learning; Escherichia coli; Computational biology; Antibacterial activity; Computer science; Biochemical engineering; Artificial intelligence; Combinatorial chemistry; Machine learning; Biology; Bacteria; Chemistry; Bioinformatics; Genetics; Gene; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004850668,0.0006640222,0.0006128159,0.0005410642,0.0002309742,0.0005408875,0.0004741634,0.0004912742,0.001453989],"category_scores_gemma":[0.0005584517,0.000242098,0.0004917512,0.0004802974,0.0003039594,0.0003277552,0.0006788999,0.0009259238,0.0004047729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000379689,"about_ca_system_score_gemma":0.0004236188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158711,"about_ca_topic_score_gemma":0.002536899,"domain_scores_codex":[0.999634,0.00007597984,0.00001949599,0.00006635788,0.0001381638,0.00006591122],"domain_scores_gemma":[0.9998035,0.00006959293,0.00002789533,0.00003157268,0.000028819,0.00003869177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004428739,0.001037616,0.003739072,0.0001674207,0.000116203,0.0002459177,0.00002813137,0.0515506,0.8993475,0.0009094774,0.001769646,0.04064548],"study_design_scores_gemma":[0.0001221448,0.001900308,0.00646203,0.00001939029,0.00007547749,0.0002276948,0.00003414141,0.2970396,0.6901782,0.0007254548,0.003179644,0.00003593404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95775,0.000956201,0.03314824,0.0006597145,0.00005665946,0.0001218745,0.002104688,0.001036532,0.004166233],"genre_scores_gemma":[0.9743312,0.000425144,0.02185945,0.0002004,0.000007688493,0.00006238739,0.001234212,0.00004841686,0.00183108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001453989,"threshold_uncertainty_score":0.004864097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01423012880960487,"score_gpt":0.2428417613073244,"score_spread":0.2286116324977196,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}